**7. Conclusions**

In conclusion, we proposed a novel MCSCNN–LSTM to forecast the electricity consumption at di fferent durations accurately and robustly only by using the self-history data. The comparative analysis has shown that the proposed hybrid deep model MCSCNN–LSTM reaches state-of-the-art performance. The proposed model is compared to other excellent deep learning-based methods to confirm the e fficiency and robustness. We run ten times for each model on three data sets to evaluate fairly. The results indicate that our proposed deep model is not sensitive to the initial settings and stable. We compare the forecasted results with other methods to prove that the proposed method can extract more detailed patterns. We also confirmed the necessity of each part in the proposed deep model by comparing the MAPE of each part for electricity forecasting at di fferent durations. We proved that the parallel structure of CNN–LSTM is more potent than conventional stacked CNN–LSTM. We also analyzed the internal feature maps to confirm the feature extraction capacity of each part, and the results show CNN can extract global features; LSTM, and statistic components are in charge of detailed pattern extraction. Some individual experimental cases are designed to validate their excellent transfer learning capacity. We confirmed the proposed MCSCNN–LSTM has excellent multi-step forecasting capacity for STF, MTF, and LTF, respectively. The proposed MCSCNN–LSTM can accurately and stably predict the irregular patterns of electricity consumption at di fferent durations by only using self-history data and have a good transfer learning capacity, which can be easy to extend to other forecasting tasks.

In this paper, we designed the networks empirically. Setting proper hyperparameters can effectively improve forecasting performance. In the feature, we will use deep reinforcement learning to automatically build the model and choose the better hyperparameters of MCSCNN–LSTM for electricity consumption forecasting.

**Author Contributions:** Designed algorithm, X.S. and C.-S.K.; Performed simulations, X.S.; Preprocessed and analyzed the data X.S. and P.S.; Wrote the paper, X.S.; Provide ideas to improve the performance of algorithm, X.S. and C.-S.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the Technology Innovation Program (20004205, The development of smart collaboration manufacturing innovation service platform in textile industry by producer-buyer B2B connection funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea)).

**Conflicts of Interest:** The authors declare no conflicts of interest.
